Evidence mapPaperPMID 39446418Full record

ArticleJMIR research protocols2024

Current State of Connected Sensor Technologies Used During Rehabilitation Care: Protocol for a Scoping Review.

Michelle R Rauzi, Rachael B Akay, Swapna Balakrishnan, Christi Piper, Denise Gobert, Alicia Flach

Abstract read
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michelle R RauziDenver/Seattle Center of Innovation for Veteran-centered and Value Driven Care, Rocky Mountain Regional VA Medical Center, Aurora, CO, United States.ORCID 0000-0002-8234-5015
Rachael B AkayPhysical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.ORCID 0000-0001-5778-5791
Swapna BalakrishnanInterprofessional Health Sciences Ph.D. Program, Department of Rehabilitation and Movement Sciences, University of Vermont, Burlington, VT, United States.ORCID 0000-0003-1522-8879
Christi PiperStrauss Health Sciences Library, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.ORCID 0000-0001-6344-9532
Denise GobertDepartment of Physical Therapy, College of Health Professions, Texas State University, Round Rock, TX, United States.ORCID 0000-0003-2757-8075
Alicia FlachExercise Science, University of South Carolina, Columbia, SC, United States.ORCID 0000-0001-9745-7039

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConnected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations.

objectiveThis study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions.

methodsThe Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness.

resultsWe conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024.

conclusionsResults from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes.

trial registrationOpen Science Framework jys53; https://osf.io/jys53. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60496.

Indexed as

RehabilitationHumansResearch DesignScoping Reviews as Topicconnected sensor technologydigital healthmHealthmobile healthrehabilitationrehabilitation careremote monitoringtelehealthwearableswearable technology

Identifiers

PMID39446418
PMCPMC11544342

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.